Vehicle trajectory tracking control method, device, equipment and medium

Abstract

The invention provides a vehicle trajectory tracking control method and device, equipment and a medium. In the method, a preview point corresponding to a vehicle and a track projection point of the preview point on a target driving track are determined based on current state information of the vehicle; on the basis of the current state information of the vehicle and the track projection point, a local deviation correction track corresponding to the vehicle is generated in real time, and the local deviation correction track is used for connecting the vehicle position to the track projection point and is continuous in the tangent direction of vehicle running; on the basis of the local deviation rectification track and the current state information, a feedforward control quantity used for controlling the vehicle driving state and feedback control quantities corresponding to at least two variables are obtained; and fusing the feedforward control quantity and the feedback control quantity, generating a control instruction and outputting the control instruction. The scheme is used for solving the problems of insufficient stability and poor anti-interference capability of a road trajectory tracking algorithm in vehicle simulation in a complex environment in related technologies.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle trajectory tracking and control method, device, equipment and medium. Background Technology

[0002] In the testing of autonomous driving systems and the development of advanced driver assistance functions based on multibody dynamics simulation, high-precision road trajectory tracking control is a core and critical component. A common functional testing method involves simulating the ability of a human driver or autonomous driving system to control a vehicle along a desired path, and then evaluating the vehicle's handling performance, ride comfort, and the effectiveness of the autonomous driving algorithm based on the control effect. The realism and reliability of the trajectory tracking control algorithm are crucial to the confidence level of vehicle design verification and algorithm iteration. A highly realistic trajectory tracking control algorithm can also significantly reduce the cost and risk of real-vehicle testing.

[0003] In related technologies, commonly used trajectory tracking algorithms typically employ a point tracking strategy and independently calculate control commands in each control cycle. This results in discontinuous curvature of the generated path, causing abrupt changes in steering control quantities. This not only causes lateral and longitudinal oscillations in the vehicle, impairing the realism and comfort of the simulation, but also exhibits deficiencies in response lag and insufficient correction capability when facing complex working conditions with dynamic changes or disturbances. Summary of the Invention

[0004] The vehicle trajectory tracking control method, apparatus, equipment, and medium provided in this application are intended to solve the problems of insufficient stability and poor anti-interference ability of road trajectory tracking algorithms in vehicle simulation under complex environments in related technologies.

[0005] In a first aspect, embodiments of this application provide a vehicle trajectory tracking and control method, including:

[0006] Based on the vehicle's current state information, determine the vehicle's corresponding aiming point and the trajectory projection point of the aiming point on the target's driving trajectory.

[0007] Based on the vehicle's current state information and trajectory projection points, a local correction trajectory corresponding to the vehicle is generated in real time. The local correction trajectory is used to connect the vehicle's position to the trajectory projection points and is continuous in the tangential direction of the vehicle's travel.

[0008] Based on the local correction trajectory and current state information, the feedforward control quantity and the feedback control quantity corresponding to at least two variables are obtained to control the vehicle's driving state.

[0009] The feedforward control quantity and the feedback control quantity are fused to generate control commands and output them.

[0010] In one possible implementation, determining the vehicle's corresponding aiming point and the trajectory projection point of the aiming point on the target driving trajectory based on the vehicle's current state information includes: acquiring the vehicle's current state information, which includes vehicle speed, the position of the vehicle's rear axle center point, heading angle, and yaw rate; projecting the vehicle's rear axle center point onto the target driving trajectory to obtain a first projection point; and determining the aiming point and the trajectory projection point based on the vehicle's current state information and the first projection point.

[0011] In one possible implementation, determining the aiming point and the trajectory projection point based on the vehicle's current state information and the first projection point includes: calculating the vehicle's lateral offset based on the coordinates of the vehicle's rear axle center point and the first projection point; determining the corresponding aiming distance based on the vehicle speed and the set aiming duration, and determining the point whose distance from the vehicle's current position along the current heading to the aiming distance is the aiming point; projecting the aiming point onto the target's driving trajectory to obtain the trajectory projection point, and determining the position and orientation angle information of the trajectory projection point.

[0012] In one possible implementation, based on the vehicle's current state information and trajectory projection points, a local correction trajectory corresponding to the vehicle is generated in real time, including: using the current position of the vehicle's rear axle center point and heading angle as the trajectory start boundary condition, and using the position and direction angle of the trajectory projection point as the trajectory end boundary condition; based on the trajectory start boundary condition and trajectory end boundary condition, a parameterized curve is generated in real time, and the parameterized curve is determined as the local correction trajectory, wherein the local correction trajectory satisfies position continuity and tangential direction continuity.

[0013] In one possible implementation, the parameterized curve is obtained based on a cubic polynomial curve, a higher-order polynomial curve of degree three or above, a trigonometric interpolation curve, or a cyclotron curve.

[0014] In one possible implementation, based on the local correction trajectory and current state information, a feedforward control quantity for controlling the vehicle's driving state and a feedback control quantity corresponding to at least two variables are obtained, including: determining the curvature at the starting point of the local correction trajectory as the target curvature; converting the target curvature into the front wheel steering angle based on the vehicle kinematic model as the feedforward control quantity; and calculating the feedback control quantity corresponding to at least two variables based on the deviation between the actual vehicle state and the desired path.

[0015] In one possible implementation, based on the deviation between the actual vehicle state and the desired path, feedback control quantities corresponding to at least two variables are calculated, including: taking the lateral distance from the rear axle center point of the vehicle to the local correction trajectory as the lateral deviation, and inputting the lateral deviation into a first PID controller to output a first feedback control component; taking the difference between the current yaw rate of the vehicle and the desired yaw rate at the current target point of the local correction trajectory as the yaw rate deviation, and inputting the yaw rate deviation into a second PID controller to output a second feedback control component; and adding the first feedback control component and the second feedback control component to obtain the feedback control quantity.

[0016] Secondly, embodiments of this application provide a vehicle trajectory tracking and control device, comprising:

[0017] The determination module is used to determine the vehicle's corresponding aiming point and the trajectory projection point of the aiming point on the target driving trajectory based on the vehicle's current state information.

[0018] The processing module is used to generate a local correction trajectory for the vehicle in real time based on the vehicle's current state information and trajectory projection points. The local correction trajectory is used to connect the vehicle's position to the trajectory projection points and is continuous in the tangential direction of the vehicle's travel.

[0019] The calculation module is used to obtain the feedforward control quantity and the feedback control quantity corresponding to at least two variables for controlling the vehicle's driving state, based on the local correction trajectory and the current state information.

[0020] The generation module is used to fuse feedforward control signals and feedback control signals to generate control commands and output them.

[0021] Thirdly, embodiments of this application provide an electronic device, including:

[0022] Processor, memory, communication interface;

[0023] Memory is used to store the processor's executable instructions;

[0024] The processor is configured to execute the vehicle trajectory tracking control method of any one of the first aspects by executing executable instructions.

[0025] Fourthly, embodiments of this application provide a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the vehicle trajectory tracking control method of any one of the first aspects.

[0026] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the vehicle trajectory tracking control method of any one of the first aspects.

[0027] The vehicle trajectory tracking control method, device, equipment, and medium provided in this application generate a continuous local correction trajectory in the tangential direction in real time based on vehicle state and pre-aiming point information, and integrate the feedforward control quantity and multivariable feedback control quantity derived from the trajectory to generate the final control command. This effectively solves the problem of abrupt control command changes caused by discontinuous trajectory curvature in related technologies. Furthermore, through the composite control mechanism of feedforward and feedback, it effectively overcomes the shortcomings of single feedback in complex environments, such as response lag and insufficient anti-interference capability, achieving smooth vehicle control commands and significantly reducing impact and vehicle oscillation during steering. It also combines the rapid response characteristics of feedforward control with the strong robustness of multivariable feedback control, enabling the vehicle to maintain high-precision trajectory tracking capability even under complex conditions such as high-speed cornering, effectively improving the realism of vehicle simulation and the comfort and stability of actual driving. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0029] Figure 1 An application scenario diagram of the vehicle trajectory tracking control method provided in this application;

[0030] Figure 2 A schematic flowchart of a vehicle trajectory tracking control method provided in one embodiment of this application;

[0031] Figure 3 A schematic flowchart of a vehicle trajectory tracking control method provided in another embodiment of this application;

[0032] Figure 4 for Figure 3 The diagram shows the positional relationships of some parameters involved in the embodiment shown.

[0033] Figure 5 A schematic diagram of the structure of a vehicle trajectory tracking control device provided in another embodiment of this application;

[0034] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application.

[0035] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0036] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0037] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0038] In multibody dynamics simulations, high-precision vehicle trajectory control is a core foundation for verifying vehicle handling stability, autonomous driving algorithms, and advanced driver assistance system (ADAS) functions. The realism of its control effect directly determines the confidence level of the simulation results, which is crucial for reducing the cost and risk of real-vehicle testing. However, complex simulation environments pose significant challenges to trajectory control. The core difficulty lies in how to simultaneously ensure the smoothness of control commands to simulate the gentle operation of a human driver, and how to maintain the system's rapid response capability and strong anti-interference ability in the face of model uncertainties, external disturbances, and complex road conditions, ensuring the stability and accuracy of the tracking process.

[0039] In related technologies, traditional pre-aiming trajectory tracking algorithms mainly employ point tracking strategies. These methods independently solve for the geometric path in each control cycle, resulting in discontinuous path curvature. This leads to abrupt changes in control commands and vehicle oscillations when the vehicle is driving on complex road conditions. Furthermore, this strategy primarily relies on a single feedback correction mechanism, resulting in inherent lag. When facing continuously changing dynamic conditions, it frequently exhibits insufficient stability and poor robustness.

[0040] The vehicle trajectory tracking control method provided in this application generates a continuous local correction trajectory in the tangential direction in real time, and analyzes the feedforward control quantity based on it. Then, it is fused with the multivariable feedback control quantity. By constructing a smooth trajectory, the source of sudden changes in control commands is fundamentally eliminated. The feedforward control is used to achieve fast response, and the multivariable feedback mechanism is used to comprehensively correct the tracking error, thereby synergistically solving the problem of achieving both command smoothness and system robustness.

[0041] Figure 1 This is a schematic diagram illustrating an application scenario of the vehicle trajectory tracking and control method provided in this application, such as... Figure 1 As shown, during the vehicle trajectory tracking control process, the server 100 obtains the predetermined target trajectory curve 110 and the vehicle's current state information 120, and generates control instructions 130 to guide the vehicle model to drive, so as to guide the vehicle to move onto the target trajectory curve 110.

[0042] It should be noted that, Figure 1 The scenario shown includes a server, a target trajectory curve, vehicle current status information, and control commands. Only one or a specific number of these are used as examples for illustration, but this disclosure is not limited to this. That is to say, the number of servers, target trajectory curves, vehicle current status information, and control commands can be arbitrary.

[0043] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0044] Figure 2 A flowchart illustrating a vehicle trajectory tracking control method provided in this application is shown below. Figure 2 As shown, the method includes:

[0045] S201. Based on the vehicle's current state information, determine the vehicle's corresponding aiming point and the trajectory projection point of the aiming point on the target driving trajectory.

[0046] Specifically, this embodiment is used to illustrate the main steps of the vehicle trajectory tracking control method.

[0047] In this embodiment, the execution entity is a computer, processor, server, etc. that runs a multibody dynamics simulation analysis and can control the driver model or vehicle model in the vehicle, so that the vehicle model can simulate driving along a preset trajectory in the simulation environment. For the convenience of subsequent description, they are collectively referred to as servers.

[0048] The current state information is a set of state variables that the server obtains in real time from the internally running vehicle dynamics model, so that the controller can determine the current state of the vehicle model. The specific current state information can be configured according to actual needs, such as the vehicle's location coordinates, speed, direction of travel, longitudinal acceleration, lateral acceleration, etc., to comprehensively describe the vehicle's transient state.

[0049] The server determines the aiming point and trajectory projection point mainly through coordinate transformation and geometric calculation.

[0050] Specifically, the server can project the vehicle's position coordinates onto the target driving trajectory to quantify the vehicle's current lateral offset, and then use this information for subsequent feedback control.

[0051] The aiming point is a virtual point in front of the vehicle, used to reflect a point a certain distance ahead of the vehicle. It can be compared to the point of view that the driver observes while driving. Generally, the faster the driving speed, the farther the aiming point is from the vehicle. Similarly, the faster the vehicle is driven, the farther the driver needs to see.

[0052] The aiming point can be determined based on an adaptive aiming algorithm for path curvature. In this case, the server will query the curvature change of the target trajectory within a certain range ahead in real time. If it is detected that it is about to enter a curve, the aiming distance will be shortened appropriately to improve tracking accuracy; if it is about to enter a straight road, the aiming distance will be extended to improve driving stability.

[0053] The trajectory projection point is the closest point on the global target trajectory curve corresponding to the pre-aiming point. It can be understood as the entry point on the target trajectory that can be seen from the driver's position (instead of driving directly in a direction perpendicular to the target trajectory curve, because this would require a sudden change in the vehicle's driving direction and speed, which does not match the actual vehicle driving situation).

[0054] In addition to determining the trajectory projection point itself, it is also necessary to determine the geometric properties of the target trajectory curve at that point, such as its position and orientation angle. This provides an endpoint constraint for generating a local trajectory that is consistent with the global path direction, so that the vehicle can smoothly return to the target trajectory curve along the local trajectory curve.

[0055] S202. Based on the vehicle's current state information and trajectory projection points, generate the corresponding local correction trajectory for the vehicle in real time.

[0056] The local correction trajectory is used to connect the vehicle position to the trajectory projection point and is continuous in the tangential direction of the vehicle's travel.

[0057] Specifically, the local correction trajectory is a reference path that is only valid within the current control cycle and connects the vehicle's current state with its desired future state.

[0058] Real-time generation means that at each simulation time step (e.g., 10 milliseconds), the server recalculates the trajectory based on the latest vehicle status and aiming point information, and makes the vehicle travel along this real-time generated trajectory, thus forming a dynamic closed-loop control process.

[0059] The continuity of the tangent direction here is used to ensure that the trajectory curve is not only continuous in position (i.e., there are no discontinuities), but also that its direction (i.e., the first derivative) changes continuously without abrupt changes.

[0060] Geometrically, this avoids sharp angles or abrupt changes in direction at the connection points of the trajectory, thereby fundamentally eliminating curvature abrupt changes and ensuring the realization of smooth steering control.

[0061] S203. Based on the local correction trajectory and current state information, obtain the feedforward control quantity and the feedback control quantity corresponding to at least two variables for controlling the vehicle's driving state.

[0062] Specifically, the server obtains feedforward control quantities by analyzing the local correction trajectory, thereby realizing open-loop control based on model prediction, and providing the vehicle model with active and forward-looking basic steering actions.

[0063] Specifically, the server calculates the curvature of the local correction trajectory at the starting point (i.e., the vehicle's current position). Curvature is a geometric quantity that characterizes the degree of curvature of a curve. For a local correction trajectory, its curvature can be calculated using the first and second derivatives of the curve. After obtaining this target curvature, the server converts it into the required front wheel steering angle based on a vehicle kinematics model (such as a simplified bicycle model). This allows the vehicle to achieve the steering angle needed to drive based on the currently generated local correction trajectory, enabling the vehicle to respond quickly to changes in the path.

[0064] At the same time, the server can also calculate feedback control quantities in parallel to correct the errors of the feedforward model, thereby dealing with various external disturbances.

[0065] Feedback control variables are generated based on multiple variables. In practical applications, specific variables may include lateral deviation, yaw rate deviation, and yaw angle deviation, thereby allowing for more precise control of the vehicle's attitude through the combination of multiple variables.

[0066] For example, the lateral deviation feedback control is used to guide the vehicle back onto the target trajectory curve, while the yaw rate deviation feedback control is responsible for suppressing the vehicle's oversteer or understeer tendencies to ensure vehicle attitude stability, and the yaw angle deviation is used to further fine-tune the vehicle's steering angle to ensure smooth vehicle driving and steering.

[0067] This multivariate collaborative correction mechanism eliminates tracking errors more comprehensively and quickly, thereby significantly enhancing the robustness of vehicle trajectory tracking control under complex operating conditions.

[0068] S204. Combine the feedforward control quantity and the feedback control quantity to generate control commands and output them.

[0069] Specifically, the fusion operation here is mainly carried out by linear superposition or vector superposition, that is, by directly adding the feedforward control quantity, the feedback control component based on lateral deviation, and the feedback control component based on yaw rate deviation, as the control parameters for controlling vehicle driving.

[0070] In some embodiments, to further improve performance, the server can also incorporate an adaptive weighted fusion method based on operating conditions, such as monitoring the current simulation conditions, including vehicle speed and lateral acceleration. During high-speed steady-state driving, the server can appropriately increase the weight of the feedforward quantity to fully leverage its rapid response advantage; while during low-speed, high-curvature steering or when there is continuous lateral wind interference, the weight of the feedback control quantity can be appropriately increased to enhance the system's anti-interference capability.

[0071] The final control commands generated are mainly the front wheel angle or the equivalent steering wheel angle (and may also include corrections for vehicle speed or acceleration).

[0072] The server takes this instruction as an input signal and outputs it to the vehicle multibody dynamics model running inside it, which drives the steering wheel or steering actuator of the virtual vehicle, thereby adjusting the vehicle's driving state.

[0073] After one simulation time step, the server will obtain new vehicle state information from the dynamic model again and start the next control cycle. This cycle repeats to form a closed-loop control system, thereby enabling the vehicle to autonomously track the target trajectory with high precision and high smoothness throughout the simulation process.

[0074] The vehicle trajectory tracking control method provided in this application generates a continuous local correction trajectory in the tangential direction in real time based on vehicle state and pre-aiming point information, and integrates the feedforward control quantity and multivariable feedback control quantity derived from the trajectory to generate the final control command. This effectively solves the problem of abrupt control command changes caused by discontinuous trajectory curvature in related technologies. Furthermore, through the composite control mechanism of feedforward and feedback, it effectively overcomes the shortcomings of single feedback in complex environments, such as response lag and insufficient anti-interference capability, achieving smooth vehicle control commands and significantly reducing impact and vehicle oscillation during steering. It also combines the rapid response characteristics of feedforward control with the strong robustness of multivariable feedback control, enabling the vehicle to maintain high-precision trajectory tracking capability even under complex conditions such as high-speed cornering, effectively improving the realism of vehicle simulation and the comfort and stability of actual driving.

[0075] Figure 3 This is a schematic diagram of another vehicle trajectory tracking control process provided in an embodiment of this application. Figure 4 for Figure 3 The schematic diagram of the positional relationship of some parameters involved in the embodiment shown is combined with... Figure 3 and Figure 4 As shown, in this embodiment... Figure 2 Based on the embodiments, the specific process of the vehicle trajectory tracking control method is described in detail, which includes:

[0076] S301. Obtain the current status information of the vehicle.

[0077] The current status information includes vehicle speed, the position of the rear axle center point, heading angle, and yaw rate.

[0078] Specifically, the server reads data in real time from the integrated vehicle multibody dynamics model.

[0079] The position of the vehicle's rear axle center point is usually used to simplify the calculation of the vehicle model's position; the heading angle describes the angle between the vehicle's longitudinal axis and the global coordinate system's X-axis, representing the vehicle's orientation; and the yaw rate reflects the angular velocity of the vehicle's rotation around its vertical axis, and is a state variable characterizing the vehicle's stability.

[0080] In the simulation, the server can receive this data through a software interface at fixed simulation steps (e.g., 1 millisecond or 10 milliseconds).

[0081] S302. Project the center point of the rear axle of the vehicle onto the target driving trajectory to obtain the first projection point.

[0082] Specifically, the server performs projection through geometric calculations.

[0083] Specifically, the target driving trajectory is typically represented by an ordered sequence of discrete path points within the server. The server iterates through this sequence and finds the path point closest to the center point of the vehicle's rear axle, which is the first projection point.

[0084] In some embodiments, the server may also interpolate between two adjacent path points to find the theoretically closest point.

[0085] S303. Based on the vehicle's current status information and the first projection point, determine the aiming point and the trajectory projection point.

[0086] Specifically, by combining the aforementioned current status information and the calculated first projection point, the server can calculate the vehicle's aiming point and trajectory projection point in real time.

[0087] Furthermore, the calculation method for specific aiming points and trajectory projection points includes the following steps:

[0088] Step A1: Calculate the lateral offset of the vehicle based on the coordinates of the center point of the rear axle and the first projection point.

[0089] Specifically, the lateral offset is the length of the vector from the center point of the rear axle of the vehicle to its first projection point, projected in a direction perpendicular to the tangent of the trajectory at the first projection point.

[0090] The server can obtain this value through vector operations, and its positive or negative sign is usually used to indicate whether the vehicle deviates to the left or right of the trajectory.

[0091] Step A2: Based on the vehicle speed and the set aiming time, determine the corresponding aiming distance, and determine the point that is the distance from the vehicle's current position along the current heading as the aiming distance as the aiming point.

[0092] Specifically, the server can adopt a fixed-time pre-aiming strategy, where the pre-aiming distance is equal to the current vehicle speed multiplied by the preset pre-aiming time.

[0093] The aiming time is an adjustable parameter, usually between 0.3 and 1.5 seconds, set by simulation engineers based on vehicle dynamics and simulation scenarios. It can also be other durations, and there are no restrictions here.

[0094] After determining the aiming distance, the server takes the center point of the vehicle's rear axle as the starting point and cuts a line segment with a length equal to the aiming distance along the current heading angle. The endpoint of this line segment is the aiming point.

[0095] Step A3: Project the pre-aiming point onto the target's driving trajectory to obtain the trajectory projection point, and determine the position and orientation angle information of the trajectory projection point.

[0096] Specifically, the server uses the same nearest point search algorithm as S302 to project the pre-aiming point obtained in step A2 onto the target driving trajectory (if interpolation was performed in this step, an interpolation algorithm can also be used here). The resulting point is the trajectory projection point.

[0097] At the same time, the server needs to obtain the orientation angle (i.e., the tangent direction of the trajectory) at the projection point of the trajectory from the global trajectory curve data.

[0098] At this point, the server has obtained two key data points for planning the local correction trajectory: the coordinates and orientation angles of the starting point (the current state of the vehicle) and the ending point (the state of the trajectory projection point).

[0099] S304. Use the current position of the rear axle center point of the vehicle and the heading angle as the starting boundary conditions of the trajectory, and use the position and heading angle of the trajectory projection point as the ending boundary conditions of the trajectory.

[0100] Specifically, the server explicitly defines the starting boundary conditions of the trajectory as follows: the position is the current position of the rear axle center point of the vehicle, and the direction angle is the current heading angle of the vehicle.

[0101] The endpoint boundary conditions are defined as follows: the position is the position of the trajectory projection point, and the direction angle is the direction angle of the trajectory projection point.

[0102] Therefore, the generated local correction trajectory can meet the requirements of the vehicle's current position and orientation towards the target point, thus ensuring a natural and smooth connection between the trajectory and the vehicle's current state and the global target path.

[0103] S305. Based on the trajectory start boundary conditions and trajectory end boundary conditions, a parameterized curve is generated in real time, and the parameterized curve is determined as the local correction trajectory.

[0104] Among them, the local correction trajectory satisfies both positional continuity and tangential direction continuity.

[0105] Specifically, the server will construct a parameterized curve that satisfies the aforementioned boundary conditions (i.e., the trajectory start boundary conditions and the trajectory end boundary conditions).

[0106] Taking a cubic polynomial curve as an example, in the natural coordinate system (with the trajectory projection point as the origin), assuming the lateral offset is a function of the longitudinal distance y = f(x), the server can represent the polynomial curve as follows:

[0107] f(x) = ax³ + bx² + cx + d,

[0108] Given a starting point (x) v y v θ v ) and endpoint (x) p yp θ p ), where θ is the direction angle (i.e., the first derivative f'(x)). Combining the aforementioned boundary conditions, a corresponding system of linear equations can be constructed to solve for the coefficients a, b, c, and d.

[0109] By solving this system of equations, a continuous C1 trajectory that is continuous in both position and tangent direction (first derivative) can be obtained.

[0110] Furthermore, the parameterized curves are obtained based on cubic polynomial curves, higher-order polynomial curves of degree three or above, trigonometric interpolation curves, or cyclotron curves.

[0111] Specifically, higher-order polynomial curves, such as fifth-order polynomial curves, can be selected as needed. In this case, they can simultaneously satisfy the boundary conditions of position, orientation angle (first derivative), and curvature (second derivative), achieving C2 continuity and making the control commands smoother.

[0112] Alternatively, a spiral curve can be selected. The curvature of this type of curve changes linearly with the length of the curve, which better matches the physical characteristics of the steering wheel rotating at a constant speed when the vehicle is turning. It conforms to the comfortable trajectory of vehicle kinematics and can provide a higher sense of realism in simulation.

[0113] S306. Determine the curvature at the starting point of the local correction trajectory as the target curvature.

[0114] Specifically, curvature is a geometric quantity that describes the degree of curvature of a curve and is directly related to the required front wheel steering angle of a vehicle.

[0115] Taking a cubic polynomial curve as an example, for a parametric curve (x(s), y(s)), its target curvature κ at s=0 (the starting point) is... target It can be calculated using the following formula:

[0116] κ target =2[3y(s) - x(s)tan(θ p - θ v )] / [x(s)] 2 ,

[0117] Where x(s) and y(s) satisfy:

[0118] x(s) = - (x p - x v sinθ v + (y p - y v cosθ v ,

[0119] y(s) = (x p - xv cosθ v + (y p - y v sinθ v .

[0120] Through this calculation, the server transforms the abstract geometric trajectory into a specific control target, namely the target curvature κ. target .

[0121] S307. Based on the vehicle kinematics model, the target curvature is converted into the front wheel steering angle as a feedforward control variable.

[0122] Specifically, the server can use a vehicle ensemble dynamics model, such as a bicycle model (i.e., the Ackermann model), to convert the front wheel steering angle.

[0123] This model establishes an approximate relationship between the front wheel steering angle and the vehicle's curvature:

[0124] Curvature κ = tan(δ) / L,

[0125] Where L is the vehicle wheelbase.

[0126] Therefore, the feedforward control quantity, i.e., the front wheel steering angle δ, is... ff It can be calculated using the following formula:

[0127] δ feedforward = arctan(κ target * L).

[0128] The feedforward control variable provides a basic steering angle required to achieve the target curvature, responds quickly, and constitutes the main part of the control command.

[0129] S308. Based on the deviation between the actual state of the vehicle and the desired path, calculate the feedback control quantities corresponding to at least two variables.

[0130] Specifically, based on the feedforward control quantity, the server can further calculate the feedback control quantity used for feedback adjustment control.

[0131] Furthermore, the specific method for determining the feedback control quantity includes the following steps:

[0132] Step B1: Take the lateral distance from the center point of the rear axle of the vehicle to the local correction trajectory as the lateral deviation, and input the lateral deviation into the first PID controller to output the first feedback control component.

[0133] Specifically, the server calculates in real time the lateral distance e from the center point of the vehicle's rear axle to the current local correction trajectory. lateral Its calculation method can be expressed as:

[0134] e lateral = d (d ≤ d max ),

[0135] e lateral = d max (d > d max ),

[0136] Where d is the trajectory offset, d max This represents the maximum trajectory offset.

[0137] Here, the server will limit the deviation results based on the physical limits of the simulated vehicle steering actuator. For example, a maximum trajectory offset d will be set. max If the calculated lateral distance e lateral If it exceeds this range, then limit it to the boundary value d. max .

[0138] This deviation is then input into a dedicated PID controller (i.e., the first PID controller). The PID controller, through its proportional (P), integral (I), and derivative (D) components, outputs a control quantity based on the history, current state, and trend of the deviation. Its output is the feedback control quantity δ. lateral , can be represented as:

[0139] ,

[0140] Among them, P lateral and I lateral It is the feedback coefficient in the first PID controller.

[0141] The function of this component is to actively guide the vehicle back to the desired local correction trajectory and eliminate lateral position errors.

[0142] Step B2: The difference between the current yaw rate of the vehicle and the expected yaw rate at the current target point of the local correction trajectory is taken as the yaw rate deviation, and the yaw rate deviation is input to the second PID controller to output the second feedback control component.

[0143] Specifically, the expected yaw rate ω p It can be calculated using the following formula:

[0144] ω p = v * κ target ,

[0145] Where v is the current vehicle speed, and κ here... target The value is determined in real time based on the vehicle's current status information, rather than a fixed value, to ensure the accuracy of feedback control.

[0146] Thus, the yaw deviation e is obtained.pitch Its calculation formula can be expressed as:

[0147] e pitch = ω v - ω p ,

[0148] By inputting this deviation into the second PID controller, the feedback control quantity δ based on the angular velocity deviation can be obtained. pitch Its calculation formula can be expressed as follows:

[0149] ,

[0150] Among them, P pitch and I pitch It is the feedback coefficient of the second PID controller.

[0151] This component can effectively suppress the vehicle's tendency to oversteer or understeer, improving cornering stability.

[0152] Step B3: Add the first feedback control component and the second feedback control component to obtain the feedback control quantity.

[0153] Specifically, the server simply linearly superimposes the outputs of the two feedback components to obtain the total feedback control quantity:

[0154] δ feedback = δ lateral + δ pitch .

[0155] This multivariate feedback structure, compared to single lateral bias feedback, can more comprehensively and quickly correct tracking errors, significantly improving the robustness of the system.

[0156] In some embodiments, when there are more types of feedback control variables, other types of feedback control variables can also be superimposed on the feedback control variables here to further improve the robustness of the system.

[0157] In some implementations, when lateral deviation persists for a long time and is large (e.g., when a vehicle deviates from its trajectory at the start of a simulation), the integral term accumulates to a very large value, leading to sluggish system response or even loss of control. Therefore, the server can employ integral separation or integral limiting algorithms to activate the integral only when the deviation is less than a certain threshold, in order to improve dynamic performance.

[0158] In some embodiments, the server can also dynamically adjust the parameters of the PID controller based on the vehicle speed v. For example, at high vehicle speeds, more conservative control parameters (i.e., smaller proportional gain) are used to ensure stability; at low vehicle speeds, more aggressive parameters (larger proportional gain) are used to improve tracking accuracy. This adaptive capability further enhances the robustness of the system.

[0159] S309. Combine the feedforward control quantity and the feedback control quantity to generate a control command and output it.

[0160] Specifically, the server fuses the outputs of the feedforward and feedback paths to generate the final control command. The most direct and effective fusion method is linear superposition, i.e., the total front wheel steering angle command:

[0161] δ = δ feedforward + δ feedback .

[0162] Feedforward δ feedforward It provides fast, smooth body steering action, while the feedback amount δ feedback The server is responsible for making precise and interference-resistant fine-tuning. It outputs this δ value to the steering system input channel of the vehicle dynamics model through the simulation software interface, thereby controlling the steering of the virtual vehicle within a simulation step. This allows for highly realistic simulation of human driving behavior in driver-in-the-loop simulation or pure offline simulation, in order to evaluate the vehicle's handling stability, ride comfort, and other performance characteristics.

[0163] Subsequently, the server returns to S301, starts the next control cycle, forms a closed-loop control, and continues until the simulation ends.

[0164] In some embodiments, the server can also limit the magnitude of the final total control command (front wheel angle) based on the physical limits of the simulated vehicle steering actuator. For example, a maximum steering angle can be set, and if the calculated command exceeds this range, it can be limited to a boundary value.

[0165] Meanwhile, to prevent sudden changes in steering angular velocity, the server can limit the rate of change of instructions between adjacent control cycles to ensure smooth changes in instructions.

[0166] The vehicle trajectory tracking control method provided in this disclosure further optimizes control performance by refining the specific implementation methods of pre-aiming point determination, local trajectory generation, and multivariate feedback calculation. It employs a fixed-time pre-aiming strategy to calculate the pre-aiming distance and utilizes advanced parametric curves such as high-order polynomials or cyclotron curves to generate a locally corrective trajectory with continuous curvature, thereby minimizing abrupt changes in control commands. Simultaneously, by separately processing lateral position deviation and yaw rate deviation using a dual-PID feedback structure, it achieves coordinated and precise correction of vehicle attitude and position. This significantly enhances the system's ability to suppress oscillations and reduce steady-state errors under complex conditions such as high-speed cornering, resulting in a trajectory tracking process that combines high smoothness and high robustness, providing crucial assurance for high-confidence simulations.

[0167] Figure 5 This is a schematic diagram of the vehicle trajectory tracking control device provided in this application, as shown below. Figure 5As shown, the vehicle trajectory tracking control device 400 provided in this embodiment includes:

[0168] The determination module 410 is used to determine the vehicle's corresponding aiming point and the trajectory projection point of the aiming point on the target driving trajectory based on the vehicle's current state information.

[0169] The processing module 420 is used to generate a local correction trajectory for the vehicle in real time based on the vehicle's current state information and trajectory projection points. The local correction trajectory is used to connect the vehicle's position to the trajectory projection points and is continuous in the tangential direction of the vehicle's travel.

[0170] The calculation module 430 is used to obtain, based on the local correction trajectory and the current state information, the feedforward control quantity for controlling the vehicle's driving state and the feedback control quantity corresponding to at least two variables.

[0171] The generation module 440 is used to fuse the feedforward control quantity and the feedback control quantity to generate control commands and output them.

[0172] In one embodiment of this disclosure, the determining module 410 is specifically used to: obtain the current state information of the vehicle, including the vehicle speed, the position of the rear axle center point, the heading angle and the yaw rate; project the rear axle center point of the vehicle onto the target driving trajectory to obtain a first projection point; and determine the aiming point and the trajectory projection point based on the current state information of the vehicle and the first projection point.

[0173] In one embodiment of this disclosure, the determining module 410 is specifically used to: calculate the lateral offset of the vehicle based on the coordinates of the rear axle center point and the first projection point; determine the corresponding pre-aiming distance based on the vehicle speed and the set pre-aiming duration, and determine the point whose distance from the current heading to the current position of the vehicle is the pre-aiming distance as the pre-aiming point; project the pre-aiming point onto the target driving trajectory to obtain the trajectory projection point, and determine the position and orientation angle information of the trajectory projection point.

[0174] In one embodiment of this disclosure, the processing module 420 is specifically used to take the current position and heading angle of the rear axle center point of the vehicle as the trajectory start boundary condition, and the position and direction angle of the trajectory projection point as the trajectory end boundary condition; based on the trajectory start boundary condition and the trajectory end boundary condition, generate a parameterized curve in real time, and determine the parameterized curve as a local correction trajectory, wherein the local correction trajectory satisfies position continuity and tangent direction continuity.

[0175] In one embodiment of this disclosure, the processing module 420 specifically includes obtaining the parameterized curve based on a cubic polynomial curve, a higher-order polynomial curve of degree three or above, a trigonometric interpolation curve, or a cyclotron curve.

[0176] In one embodiment of this disclosure, the calculation module 430 is specifically used to: determine the curvature at the starting point of the local correction trajectory as the target curvature; convert the target curvature into the front wheel steering angle based on the vehicle kinematics model, as a feedforward control quantity; and calculate the feedback control quantity corresponding to at least two variables based on the deviation between the actual state of the vehicle and the desired path.

[0177] In one embodiment of this disclosure, the calculation module 430 is specifically used to: take the lateral distance from the center point of the rear axle of the vehicle to the local correction trajectory as the lateral deviation, and input the lateral deviation to the first PID controller to output a first feedback control component; take the difference between the current yaw rate of the vehicle and the expected yaw rate at the current target point of the local correction trajectory as the yaw rate deviation, and input the yaw rate deviation to the second PID controller to output a second feedback control component; and add the first feedback control component and the second feedback control component to obtain the feedback control quantity.

[0178] The vehicle trajectory tracking control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0179] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 6 As shown, the electronic device 50 includes:

[0180] Processor 51, memory 52, and communication interface 53;

[0181] Memory 52 is used to store executable instructions of processor 51;

[0182] The processor 51 is configured to execute the technical solutions in any of the foregoing method embodiments by executing the aforementioned executable instructions.

[0183] Optionally, the memory 52 can be either standalone or integrated with the processor 51.

[0184] Optionally, when the memory 52 is a device independent of the processor 51, the electronic device 50 may further include:

[0185] Bus 54, memory 52 and communication interface 53 are connected to processor 51 through bus 54 and complete communication with each other. Communication interface 53 is used to communicate with other devices.

[0186] Optionally, the communication interface 53 can be implemented using a transceiver. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write databases, and read-only databases). The memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0187] Bus 54 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0188] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0189] The electronic device is used to execute the technical solutions in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0190] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing method embodiments.

[0191] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0192] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A vehicle trajectory tracking control method characterized by, The method comprises the following steps: determining a preview point corresponding to the vehicle and a trajectory projection point of the preview point on a target driving trajectory based on current state information of the vehicle; generating a local correction trajectory corresponding to the vehicle in real time based on the current state information of the vehicle and the trajectory projection point, wherein the local correction trajectory is used to connect the vehicle position to the trajectory projection point and is continuous in the tangent direction of the vehicle driving; obtaining a feedforward control amount for controlling the driving state of the vehicle and feedback control amounts corresponding to at least two variables based on the local correction trajectory and the current state information; fusing the feedforward control amount and the feedback control amounts to generate a control instruction and outputting the control instruction.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining the current state information of the vehicle, wherein the current state information comprises the vehicle speed, the position of the rear axle center point of the vehicle, the heading angle and the yaw rate; projecting the rear axle center point of the vehicle to the target driving trajectory to obtain a first projection point; determining the preview point and the trajectory projection point based on the current state information of the vehicle and the first projection point.

3. The method of claim 2, wherein, The method comprises the following steps: calculating the lateral offset of the vehicle based on the coordinates of the rear axle center point of the vehicle and the first projection point; determining the preview distance corresponding to the vehicle speed and the set preview time, and determining the point with a distance of the preview distance from the current position of the vehicle along the current heading direction as the preview point; projecting the preview point to the target driving trajectory to obtain the trajectory projection point, and determining the position and direction angle information of the trajectory projection point.

4. The method of claim 2, wherein, The method comprises the following steps: taking the current position and the heading angle of the rear axle center point of the vehicle as the starting boundary condition of the trajectory, and taking the position and the direction angle of the trajectory projection point as the ending boundary condition of the trajectory; generating a parametric curve in real time based on the starting boundary condition of the trajectory and the ending boundary condition of the trajectory, and determining the parametric curve as the local correction trajectory, wherein the local correction trajectory satisfies the conditions of position continuity and tangent direction continuity.

5. The method of claim 4, wherein, The parametric curve is obtained based on a cubic polynomial curve, a high-order polynomial curve with a degree higher than three, a trigonometric function interpolation curve or a clothoid curve.

6. The method according to any one of claims 1 to 5, characterized in that, The method comprises the following steps: determining the curvature at the starting point of the local correction trajectory as a target curvature; converting the target curvature into a front wheel steering angle based on the kinematic model of the vehicle, as the feedforward control amount; calculating the feedback control amounts corresponding to the at least two variables based on the deviation between the actual state of the vehicle and the expected path.

7. The method of claim 6, wherein, The method comprises the following steps: A lateral distance from a center point of a rear axle of the vehicle to the local correction trajectory is taken as a lateral deviation, and the lateral deviation is input to a first PID controller to output a first feedback control component; A difference between a current yaw rate of the vehicle and a desired yaw rate at a current target point of the local correction trajectory is taken as a yaw rate deviation, and the yaw rate deviation is input to a second PID controller to output a second feedback control component; The first feedback control component and the second feedback control component are added to obtain the feedback control amount.

8. A vehicle trajectory tracking control device characterized by comprising: The method comprises: A determination module is configured to determine a preview point corresponding to the vehicle and a trajectory projection point of the preview point on a target trajectory based on current state information of the vehicle; A processing module is configured to generate a local correction trajectory corresponding to the vehicle in real time based on the current state information of the vehicle and the trajectory projection point, wherein the local correction trajectory is used to connect a vehicle position to the trajectory projection point and is continuous in a tangent direction of vehicle travel; A calculation module is configured to obtain a feedforward control amount for controlling a vehicle travel state and feedback control amounts corresponding to at least two variables based on the local correction trajectory and the current state information; A generation module is configured to fuse the feedforward control amount and the feedback control amounts to generate a control instruction and output the control instruction.

9. An electronic device, comprising: The method comprises: A processor, a memory, and a communication interface; The memory is configured to store executable instructions of the processor; The processor is configured to execute the vehicle trajectory tracking control method according to any one of claims 1 to 7 by executing the executable instructions.

10. A readable storage medium, having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the vehicle trajectory tracking control method according to any one of claims 1 to 7.

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